Biometrics have been successfully utilised as a means for identifying an individual. At present, there are a number of methods employed for biometric identification of individuals. One of the oldest techniques is that of matching dental records or bite impressions for a given individual. While this technique is effective, its application is somewhat limited. Typically, the use of technique requires the Subject to provide a bite sample or the subject to be deceased to enable comparison of their teeth with dental records. In addition, the accuracy of the method can be affected in cases where an individual has had dental work performed without a record being kept of the work.
Another form of biometric identification which is popular and has a well established history is print identification. Traditionally, the technique has involved recording an image of the fingerprint, handprint, footprint by inking the relevant area and making an impression of the print on paper (template image). The patterns in the print formed by the individual's minutiae are then compared against a sample to determine a match with the template print. More recently, print analysis has gone digital; this has allowed print recognition to be utilised in a number of security applications to verify the identity of users prior to granting them access to system, building, etc. One of the most popular forms of print recognition for digital security and identification systems is fingerprint recognition.
One of the most critical steps in automated fingerprint authentication system is acquisition of the image of the print, as it determines the final fingerprint image quality, which has a drastic effect on the overall system performance. There are different types of fingerprint readers on the market, but the basic idea behind each is to measure the physical difference between ridges and valleys.
The procedure for capturing a fingerprint using a sensor consists of rolling or touching with the finger onto a sensing area, which according to the physical principle in use (optical, ultrasonic, capacitive or thermal) captures the difference between valleys and ridges. Once the image is captured, it then undergoes smoothing; a binary image of the print is then generated. The binary image then undergoes thinning to further sharpen the image. Once the thinning process is complete, the ridge reconstruction is performed. The further processing of the image to produce a template image is dictated based on the image recognition technique being utilised.
Presently, there are two main forms of template recognition techniques utilising pattern matching and minutiae feature matching. With pattern-based algorithms, the template contains the type, size and orientation of patterns within the aligned fingerprint image. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match. Minutiae feature matching analyses the geometric characteristics such as distance and angle between standard minutiae and its neighbouring minutiae based on the analysis of the image-processed feature data. After the analysis, all the minutiae pairs have some kind of geometric relationship with their neighbouring minutiae, and the relationship will be used as basic information for local similarity measurement
One of the problems associated with fingerprint scanning is that when a finger touches or rolls onto a surface, the elastic skin deforms. The quantity and direction of the pressure applied by the user, the skin conditions—wear due to manual labour, age, chemotherapy—and the projection of an irregular 3D object (the finger) onto a 2D flat plane introduce distortions, noise and inconsistencies in the captured fingerprint image. These problems result in inconsistent, irreproducible and non-uniform irregularities in the image. During each acquisition, therefore, the results of the imaging are different and uncontrollable. The representation of the same fingerprint changes every time the finger is placed on the sensor plate, increasing the complexity of any attempt to match fingerprints, impairing the system performance and consequently reliability.
In addition to the potential performance and accuracy issues posed by image acquisition, it is possible to fool fingerprint readers through various means i.e. false prints made from an image of a fingerprint. More recently, the television series ‘Mythbusters’ found a way to convert fingerprints lifted from the hand to a photographic form that the sensor would accept. For obvious reasons, they refuse to reveal the technique.
Another popular form of biometric recognition is that of iris recognition. With this process, an image of the eye is captured. The iris-recognition algorithm then localizes the inner and outer boundaries of the iris (pupil and limbus) in the image. Further subroutines detect and exclude eyelids, eyelashes, and specular reflections that often occlude parts of the iris. The set of pixels containing only the iris is then normalized by a rubber-sheet model to compensate for pupil dilation or constriction. The normalised image is then analysed to extract a bit pattern encoding the information needed to compare the capture image with a template image constructed for the individual. In the case of Daugman's algorithms, a Gabor wavelet transform is used. The result is a set of complex numbers that carry local amplitude and phase information about the iris pattern. In Daugman's algorithms, most amplitude information is discarded, and the 2048 bits representing an iris pattern consist of phase information. Discarding the amplitude information ensures that the template remains largely unaffected by changes in illumination or camera gain, and contributes to the long-term usability of the biometric template. For identification or verification, a template created by imaging an iris is compared to stored template(s) in a database. If the Hamming distance is below the decision threshold, a positive identification has effectively been made because of the statistical extreme improbability that two different persons could agree by chance (“collide”) in so many bits, given the high entropy of iris templates.
As in the case of fingerprint recognition, iris recognition systems have a number of faults. Many commercial iris scanners can be easily fooled by a high quality image of an iris or face in place of the real thing. The scanners are often difficult to adjust and can become challenging for multiple people of different heights to use in succession. The accuracy of scanners can be affected by changes in lighting, dark brown irises, and restricted population patterns. Iris recognition is very difficult to perform at a distance larger than a close distance—less than 1 meter—and additionally if the person to be identified is not cooperating by holding the head still and looking into the camera. However, several academic institutions and biometric vendors are developing products that claim to be able to identify subjects at distances of up to 10 meters (“standoff iris” or “iris at a distance” as well as “iris on the move” for persons walking at speeds up to 1 meter/sec). As with other photographic biometric technologies, iris recognition is susceptible to poor image quality, with associated failure rates in enrollment. Researchers have tricked iris scanners using images generated from digital codes of stored irises. Criminals could exploit this flaw to steal the identities of other people.
Clearly, it would be advantageous to provide an apparatus, system and method which would mitigate the risks associated with falsification of biometric information for the misappropriation of personal data. It would also be advantageous to provide a system and method of identification that would reduce the likelihood of false positives or misreads during the identification process.